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Keywords: machine learning
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Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-200009-MS
... gives fair estimates of fracture spatial evolutions. machine learning reservoir characterization intervención de pozos petroleros production monitoring modeling & simulation artificial intelligence hydraulic fracturing reservoir simulation upstream oil & gas production forecasting...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-200019-MS
... Abstract Recently machine learning has being extensively deployed for oil and gas industry for improving result and expedite process. However, the black box models do not explain their prediction which considered as a barrier to adopt machine learning. This paper is about optimizing hydraulic...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-199967-MS
... Abstract This paper builds on Klenner et al. 2018 , which utilized machine learning to understand well-to-well communication ("Frac hits") or fracture-driven interaction (FDI) during hydraulic fracturing operations. This paper introduces an infrastructure that enahances the process for real...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-199988-MS
... comprehensive and robust approach is presented for integrating specific RTA interpretations and estimations into various steps of the history-matching process. pressure transient analysis machine learning production monitoring production forecasting optimization problem pressure transient testing...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-200003-MS
... dataset recognition neural network ball pumpdown seat operation prediction ball seat event recognition algorithm dataset slurry rate machine learning cnn model wellhead pressure computational resource deep learning convolutional neural network ball seat event u-net model Introduction...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-200000-MS
... Abstract An Augmented AI approach has been developed to optimize completion design parameters and access the full potential of unconventional assets by leveraging big data sculpting, domain-induced feature engineering, and robust and explainable machine learning models with quantified...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-200006-MS
... intelligence classification bit selection drilling operation machine learning drilling equipment dysfunction indicator metric information baseline mse value strength drilling parameter drillstring design upstream oil & gas dysfunction identification founder point chart dysfunction indicator...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, September 28–October 2, 2020
Paper Number: SPE-200021-MS
... modeling reservoir geomechanics well logging machine learning production monitoring reservoir surveillance reservoir characterization fracturing materials flow in porous media tight gas production forecasting equation of state complex reservoir estimates of resource in place artificial...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189815-MS
... properties, a regional map of break-even oil price is generated. This technique translates the gradational variation of multiple subsurface parameters into a continuous map of relative economic value, which can then be used to discuss a multitude of appraisal and development issues. machine learning...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189786-MS
... of uncertainty can also be achieved, which assists in understanding the range of parameters which can be used to successfully match the flowback data. flow in porous media history matching palisade evolver equivalent generation machine learning solver optimization problem Fluid Dynamics iteration...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189806-MS
... the most influential factors influencing the water uptake during shut-in periods after hydraulic fracturing operations. Artificial Intelligence neural network residual saturation imbibition saturation spontaneous imbibition fracture machine learning flow in porous media shale gas Upstream...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189830-MS
.... Artificial Intelligence Upstream Oil & Gas correlation machine learning neural network data preparation fluid modeling equation of state unconventional resource conference hydraulic fracturing classification unsupervised neural network classification model spe csur unconventional resource...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189811-MS
... connection time machine learning operator Drilling artificial intelligence human-machine interaction utilization society of petroleum engineers sequence Introduction Oil and gas is found deep beneath the earth's surface and is extracted by drilling a slender, often tortuous wellbore from...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189802-MS
... forecasting Modeling & Simulation hyperbolic model machine learning certainty reserves evaluation workflow Artificial Intelligence complex reservoir hyperbolic decline Upstream Oil & Gas unconventional reservoir transient linear flow Duong History Seidle gas well flow regime straight...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189808-MS
...Methodology Machine learning is a process through which computer will learn from data to find a possible pattern in the data set. This process encompasses three main components; Learning algorithm, Data, and Pattern in the data. If these three components are present, a successful learning...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189790-MS
... on these wells. Advanced machine learning and data mining algorithms of data analytics such as random forest, gradient boost, linear regression, etc. were applied on the data points to create a proxy model for the fracturing and numerical production simulator. With the gradient boost technique, over 90% accuracy...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189823-MS
... Abstract This paper presents the use of machine learning via a multiple linear regression and a neural network to solve the complex problem of optimizing completions and well designs in the Duvernay shale. Solutions were revealed that could save over a million dollars per well, along...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189791-MS
... Upstream Oil & Gas porosity pore throat resistivity equation maturation trajectory reservoir machine learning well logging Aguilera thermal maturity determination Pickett plot permeability Introduction Using the concept of a ‘Total Petroleum System (TPS)' and real data from various...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189797-MS
... in the Mexican Eagle Ford shale, which is similar to the water resistivity values found in the Texas Eagle Ford. shale gas Reservoir Characterization machine learning complex reservoir Artificial Intelligence Upstream Oil & Gas water saturation gas production reservoir pressure shale Burgos...
Proceedings Papers

Paper presented at the SPE Canada Unconventional Resources Conference, March 13–14, 2018
Paper Number: SPE-189809-MS
... normal diffusion fracture network fracture system Artificial Intelligence relation anomalous diffusion diffusion matrix machine learning Upstream Oil & Gas fracture length diffusivity equation hydraulic fracture hydraulic fracturing node unconventional reservoir fracture anomalous...

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